Methods › Computer Vision › Image Models › ProxylessNet-CPU
ProxylessNet-CPU
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
ProxylessNet-CPU is an image model learnt with the ProxylessNAS neural architecture search algorithm that is optimized for CPU devices. It uses inverted residual blocks (MBConvs) from MobileNetV2 as its basic building block.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
-
MaxUp: A Simple Way to Improve Generalization of Neural Network Training 20 Feb 2020 · 1 repository · arXiv:2002.09024Syntology ran 0 of 1 samples · 1 unverified
-
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware 2 Dec 2018 · 23 repositories · arXiv:1812.00332Syntology ran 5 of 27 samples · 22 unverified
Tasks archive 2025-07-28
8 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Image Classification | 2 |
| Few-Shot Image Classification | 1 |
| GPU | 1 |
| General Classification | 1 |
| Language Modeling | 1 |
| Language Modelling | 1 |
| Neural Architecture Search | 1 |
| image-classification | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections